COURSE 2: THE PATH TO INSIGHTS: DATA MODELS AND PIPELINES

Module 3: Optimize ETL Processes

GOOGLE BUSINESS INTELLIGENCE PROFESSIONAL CERTIFICATE

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TABLE OF CONTENT

INTRODUCTION – Optimize ETL Processes

In this comprehensive course, participants will delve into advanced optimization techniques crucial for maintaining the integrity and quality of data in sophisticated database systems. The curriculum encompasses a diverse range of strategies, including ETL quality testing, data schema validation, business rule verification, and general performance testing. As participants navigate these optimization techniques, they gain valuable insights into fortifying data pipelines against potential challenges, ensuring the reliability and accuracy of information within a business intelligence context.

A pivotal aspect of the course is the exploration of data integrity, emphasizing how built-in quality checks act as a defense mechanism against potential issues. Participants will gain a thorough understanding of how these checks contribute to safeguarding the consistency and reliability of data throughout its journey within the system. The course concludes by honing in on the meticulous verification of business rules and conducting general performance testing to guarantee that pipelines align seamlessly with the intended business objectives, thereby fortifying the foundation for informed decision-making processes. Overall, this course equips participants with the tools to proactively ensure data quality, integrity, and performance within the evolving landscape of business intelligence.

Learning Objectives

  • Discover strategies to create an ETL process that works to meet organizational and stakeholder needs and how to maintain an ETL process efficiently.
  • Introduce tools used in ETL
  • Understand the primary goals of ETL quality testing.
  • Understand the primary goals of data schema validation.
  • Develop ETL quality testing and data schema validation best practices.
  • Identify and implement appropriate test scenarios and checkpoints for QA on data pipelines.
  • Explain different methods for QA data in the pipeline.
  • Create performance testing scenarios and measure performance throughout the pipeline.
  • Verify business rules.
  • Perform general performance testing. 

PRACTICE QUIZ: TEST YOUR KNOWLEDGE: OPTIMIZE PIPELINES AND ETL PROCESSES

1. What is the business intelligence process that involves checking data for defects in order to prevent system failures?

  • Business intelligence monitoring
  • Query planning
  • Quality testing (CORRECT)
  • Data governance

Correct: Quality testing is the business intelligence process that involves checking data for defects in order to prevent system failures.

2. Fill in the blank: Completeness is a quality testing step that involves confirming that the data contains all desired ____ or components.

  • Measures (CORRECT)
  • Columns
  • Context
  • Fields

Correct: Completeness is a quality testing step that involves confirming that the data contains all desired measures or components.

3. A business intelligence professional is considering the integrity of their data throughout its life cycle. Which of the following goals do they aim to achieve? Select all that apply.

  • Data is encrypted
  • Data is consistent (CORRECT)
  • Data is accurate and complete (CORRECT)
  • Data is trustworthy (CORRECT)

Correct: They aim to achieve data accuracy, completeness, consistency, and trustworthiness. These are the key considerations of data integrity.

Correct: Their goal is to achieve data accuracy, completeness, consistency, and trustworthiness. These are the key considerations of data integrity.

PRACTICE QUIZ: TEST YOUR KNOWLEDGE: DATA SCHEMA VALIDATION

1. A team of business intelligence professionals builds schema validation into their workflows. In this situation, what goal do they want to achieve?

  • Ensure the source system data schema matches the target system data schema (CORRECT)
  • Consider the needs of stakeholders in the design of the data schema
  • Consolidate data from multiple source systems
  • Prevent two or more components from using a single resource in a conflicting way

Correct: They want to ensure the source system data schema matches the target system data schema.

2. Why is it important to ensure primary and foreign keys continue to function after data has been moved from one database system to another?

  • To read and execute coded instructions
  • To evaluate database performance
  • To provide more detail and context about the data
  • To preserve the existing table relationships (CORRECT)

Correct: It is important to ensure primary and foreign keys continue to function after data has been moved from one database system to another in order to preserve the existing table relationships.

3. Fill in the blank: A _____ describes the process of identifying the origin of data, where it has moved throughout the system, and how it has transformed over time.

  • quality test
  • business rule
  • data dictionary
  • data lineage (CORRECT)

Correct: A data lineage describes the process of identifying the origin of data, where it has moved throughout the system, and how it has transformed over time.

PRACTICE QUIZ: TEST YOUR KNOWLEDGE: BUSINESS RULES AND PERFORMANCE TESTING

1. A business intelligence professional considers what data is collected and stored in a database, how relationships are defined, the type of information the database provides, and the security of the data. What does this scenario describe?

  • Considering the impact of business rules (CORRECT)
  • Expanding scope in response to stakeholder requirements
  • Confirming that data is consistent
  • Ensuring the formal management of data assets

Correct: This scenario describes establishing business rules. A business rule is a statement that creates a restriction on specific parts of a database. It helps determine if a database is performing as intended.

2. At which point in the data-transfer process should incoming data be compared to business rules?

  • No later than 24 hours after being loaded into the database
  • Before loading it into the database (CORRECT)
  • At the same time as it is being loaded into the database
  • As soon as it has been loaded into the database

Correct: During the data-transfer process, incoming data should be compared to business rules before loading it into the database.

MODULE 3 CHALLENGE

1. A business intelligence professional wants to avoid system failures. They check over their data in order to identify missing data, inconsistent data, or any other data defects. What does this scenario describe?

  • Quality testing (CORRECT)
  • Optimizing response time
  • Data partitioning
  • Making trade-offs

2. A business intelligence professional is confirming that their data contains all desired components or measures. Which quality testing validation element does this involve?

  • Integrity
  • Completeness (CORRECT)
  • Accuracy
  • Consistency

3. A business intelligence team analyzes current data in order to confirm that stakeholders gain the most up-to-date insights in the future. In this situation, what aspect of data do they consider?

  • Redundancy
  • Timeliness (CORRECT)
  • Conformity
  • Maturity

4. Conformity is an aspect of establishing consistent data governance. What are the key tools involved with conformity? Select all that apply.

  • Combined systems
  • Data dictionaries (CORRECT)
  • Schema validation (CORRECT)
  • Data lineages (CORRECT)

5. What are the goals of schema validation? Select all that apply.

  • To establish row-based permissions
  • To preserve table relationships (CORRECT)
  • To confirm the validity of database keys (CORRECT)
  • To ensure consistent conventions (CORRECT)

6. Which of the following statements accurately describe data dictionaries and data lineages? Select all that apply.

  • A data dictionary describes the process of identifying the origin of data, where it has moved throughout the system, and how it has transformed over time.
  • A data lineage is a collection of information that describes the content, format, and structure of data objects within a database, as well as their relationships.
  • A data dictionary is a collection of information that describes the content, format, and structure of data objects within a database. (CORRECT)
  • A data lineage describes the process of identifying the origin of data, where it has moved throughout the system, and how it has transformed over time. (CORRECT)

7. Fill in the blank: Business rules affect what data is collected and stored in a database, how relationships are defined, the kind of information the database provides, and the _____ of the data.

  • Granularity
  • Security (CORRECT)
  • readability
  • maturity

8. Fill in the blank: Quality testing is the process of checking data for _____ in order to prevent system failures.

  • links
  • scalability
  • defects (CORRECT)
  • granularity

9. A data warehouse is supposed to contain weekly data, but it does not update properly. As a result, the pipeline fails to ingest the latest information. What aspect of the data is being affected in this situation?

  • Timeliness (CORRECT)
  • Redundancy
  • Conformity
  • Maturity

10. Business intelligence professionals use schema validation, data dictionaries, and data lineages while establishing consistent data governance. Which aspect of data validation does this involve?

  • Conformity (CORRECT)
  • Security
  • Context
  • Quality

11. Fill in the blank: Schema validation properties preserve table relationships, ensure consistent conventions, and ensure database _____ are still valid.

  • interfaces
  • permissions
  • keys (CORRECT)
  • models

12. Fill in the blank: A data _____ describes the process of identifying the origin of data, where it has moved throughout the system, and how it has transformed over time.

  • Dictionary
  • map
  • model
  • lineage (CORRECT)

13. What elements of database design are affected by business rules? Select all that apply.

  • The maturity of the data
  • How relationships are defined (CORRECT)
  • The security of the data (CORRECT)
  • What data is collected, stored, and provided (CORRECT)

14. A business intelligence professional establishes what data will be collected, stored, and provided in a database. They also confirm how relationships are defined and the security of the data. What process does this scenario describe?

  • Iteration
  • Database modeling
  • Optimization
  • Creating business rules (CORRECT)

15. A business intelligence professional is confirming that their data conforms to the actual entity being measured or described. Which quality testing validation element does this involve?

  • Completeness
  • Integrity
  • Accuracy (CORRECT)
  • Consistency

16. A business intelligence professional is working with a data warehouse. They perform various tasks to confirm that the data is timely and the pipeline is ingesting the latest information. For what reasons is this an important element of business intelligence? Select all that apply.

  • To map the data correctly
  • To provide relevant insights (CORRECT)
  • To ensure the data is updated properly (CORRECT)
  • To have the most current information (CORRECT)

17. Fill in the blank: A data _____ is a collection of information that describes the content, format, and structure of data objects within a database, as well as their relationships.

  • Dictionary (CORRECT)
  • model
  • lineage
  • map

18. Quality testing involves checking data for defects in order to prevent what from happening?

  • Fragmentation
  • Redundancy
  • Contention
  • System failure (CORRECT)

19. A business intelligence professional is confirming that their data is compatible and in agreement across all systems. Which quality testing validation element does this involve?

  • Consistency (CORRECT)
  • Completeness
  • Integrity
  • Accuracy

20. Fill in the blank: To ensure _____ from source to destination, business intelligence professionals use schema validation, data dictionaries, and data lineages.

  • visibility
  • security
  • conformity (CORRECT)
  • context

21. When quality testing, why does a business intelligence professional confirm data conformity?

  • To ensure the data fits the required destination format (CORRECT)
  • To ensure the data conforms to the actual entity being measured or described
  • To ensure the data is compatible and in agreement across all systems
  • To ensure the data contains all desired components or measures

Correct: When quality testing, a business intelligence professional confirms data conformity in order to ensure the data fits the required destination format.

22. Fill in the blank: A _____ is a collection of information that describes the content, format, and structure of data objects within a database, as well as their relationships.

  • data model
  • relational database
  • data lineage
  • data dictionary (CORRECT)

Correct: A data dictionary is a collection of information that describes the content, format, and structure of data objects within a database, as well as the relationships.

23. Fill in the blank: A business rule is a statement that creates a _____ on specific parts of a database.

  • field
  • gateway
  • restriction (CORRECT)
  • channel

Correct: A business rule is a statement that creates a restriction on specific parts of a database. It helps prevent errors within the system.

CONCLUSION – Optimize ETL Processes

In conclusion, this advanced optimization course serves as a strategic compass for participants navigating the intricate landscape of data management within business intelligence systems. The comprehensive exploration of optimization techniques, ranging from ETL quality testing to data schema validation and business rule verification, empowers learners to fortify data pipelines against potential challenges. The emphasis on general performance testing ensures that these pipelines not only meet but exceed the intended business needs, contributing to the seamless flow of accurate and reliable information.

The course’s in-depth focus on data integrity illuminates the significance of built-in quality checks in defending against potential problems. Participants leave with a profound understanding of how these checks serve as vigilant guardians, upholding the consistency and reliability of data throughout its lifecycle within the system. As learners conclude this journey, they emerge equipped with the knowledge and skills to proactively ensure data quality and integrity, positioning themselves as adept stewards of data-driven decision-making processes in the dynamic landscape of business intelligence.

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